Querying a model via SherlockML APIs

There are countless examples of SherlockML assisting in deployment of models. One such example was for the marketing department of a financial services company with a large email list.

In order to follow the popular topics across the financial industry every day, Data Scientists built a system in SherlockML that collected and analysed tweets from the twitter accounts of financial influencers. The aim was to compose marketing material that would appeal to their email subscribers.

The final model was productionised as a SherlockML API.

The API was then deployed and hosted on SherlockML. Daily analysis of a fresh set of tweets was also automatically performed in the background, keeping the model up-to-date.

In fact, the API call was made by an external interactive dashboard, so that the marketing team can now easily monitor the various financial topics of the day.

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